by: Kaegan Casey, Patrick Durkin, Mengchao Li, Yongbo Shu, Yiwei Yan
Import libraries and read in data
library(ggplot2)
Registered S3 method overwritten by 'dplyr':
method from
print.rowwise_df
library(plyr)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:plyr’:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(quantmod)
Loading required package: xts
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Registered S3 method overwritten by 'xts':
method from
as.zoo.xts zoo
Attaching package: ‘xts’
The following objects are masked from ‘package:dplyr’:
first, last
Loading required package: TTR
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo
Version 0.4-0 included new data defaults. See ?getSymbols.
library(scales)
library(rlist)
Registered S3 method overwritten by 'data.table':
method from
print.data.table
library(tidyverse)
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library(transformr)
library(gganimate)
library(xtable)
options(stringsAsFactors = FALSE, scipen=99999)
# weatherFull just needs to be in the same folder as this notebook .rmb file
full <- read.csv('weatherFull.csv')
Descriptive Statistics
head(full)
dim(full)
[1] 1572907 52
summary(full[,1:26])
X BEGIN_YEARMONTH BEGIN_DAY BEGIN_TIME END_YEARMONTH END_DAY
Min. : 1 Min. :195001 Min. : 1.00 Min. : 0 Min. :195001 Min. : 1.00
1st Qu.: 393228 1st Qu.:199905 1st Qu.: 7.00 1st Qu.: 900 1st Qu.:199905 1st Qu.: 8.00
Median : 786454 Median :200612 Median :15.00 Median :1515 Median :200612 Median :16.00
Mean : 786454 Mean :200422 Mean :15.02 Mean :1324 Mean :200422 Mean :16.35
3rd Qu.:1179680 3rd Qu.:201212 3rd Qu.:23.00 3rd Qu.:1820 3rd Qu.:201212 3rd Qu.:24.00
Max. :1572907 Max. :201906 Max. :31.00 Max. :2359 Max. :201906 Max. :31.00
END_TIME EPISODE_ID EVENT_ID STATE STATE_FIPS YEAR
Min. : 0 Min. : 1 Min. : 3 Length:1572907 Min. : 1.0 Min. :1950
1st Qu.:1200 1st Qu.: 59043 1st Qu.: 415503 Class :character 1st Qu.:19.0 1st Qu.:1999
Median :1613 Median : 117466 Median : 831695 Mode :character Median :30.0 Median :2006
Mean :1483 Mean : 637232 Mean : 3603266 Mean :31.7 Mean :2004
3rd Qu.:1909 3rd Qu.: 1163492 3rd Qu.: 5562408 3rd Qu.:45.0 3rd Qu.:2012
Max. :2359 Max. :990000001 Max. :990000003 Max. :99.0 Max. :2019
NA's :232239 NA's :1
MONTH_NAME EVENT_TYPE CZ_TYPE CZ_FIPS CZ_NAME
Length:1572907 Length:1572907 Length:1572907 Min. : 0.0 Length:1572907
Class :character Class :character Class :character 1st Qu.: 25.0 Class :character
Mode :character Mode :character Mode :character Median : 65.0 Mode :character
Mean : 94.2
3rd Qu.:117.0
Max. :876.0
WFO BEGIN_DATE_TIME CZ_TIMEZONE END_DATE_TIME INJURIES_DIRECT
Length:1572907 Length:1572907 Length:1572907 Length:1572907 Min. : 0.0000
Class :character Class :character Class :character Class :character 1st Qu.: 0.0000
Mode :character Mode :character Mode :character Mode :character Median : 0.0000
Mean : 0.0967
3rd Qu.: 0.0000
Max. :1700.0000
INJURIES_INDIRECT DEATHS_DIRECT DEATHS_INDIRECT DAMAGE_PROPERTY
Min. : 0.0000 Min. : 0.0000 Min. : 0.00000 Length:1572907
1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00000 Class :character
Median : 0.0000 Median : 0.0000 Median : 0.00000 Mode :character
Mean : 0.0084 Mean : 0.0116 Mean : 0.00161
3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.00000
Max. :2400.0000 Max. :638.0000 Max. :20.00000
summary(full[,27:52])
DAMAGE_CROPS SOURCE MAGNITUDE MAGNITUDE_TYPE FLOOD_CAUSE
Length:1572907 Length:1572907 Min. : 0.0 Length:1572907 Length:1572907
Class :character Class :character 1st Qu.: 0.9 Class :character Class :character
Mode :character Mode :character Median : 1.8 Mode :character Mode :character
Mean : 25.5
3rd Qu.: 52.0
Max. :22000.0
NA's :653424
CATEGORY TOR_F_SCALE TOR_LENGTH TOR_WIDTH TOR_OTHER_WFO
Min. :1.0 Length:1572907 Min. : 0.0 Min. : 0.0 Length:1572907
1st Qu.:1.0 Class :character 1st Qu.: 0.0 1st Qu.: 0.0 Class :character
Median :1.0 Mode :character Median : 0.0 Median : 0.0 Mode :character
Mean :1.4 Mean : 0.9 Mean : 32.3
3rd Qu.:1.0 3rd Qu.: 0.0 3rd Qu.: 10.0
Max. :5.0 Max. :2315.0 Max. :5280.0
NA's :1572581 NA's :1307840 NA's :1307840
TOR_OTHER_CZ_STATE TOR_OTHER_CZ_FIPS TOR_OTHER_CZ_NAME BEGIN_RANGE BEGIN_AZIMUTH
Length:1572907 Min. : 1.0 Length:1572907 Min. : 0.0 Length:1572907
Class :character 1st Qu.: 47.0 Class :character 1st Qu.: 0.0 Class :character
Mode :character Median : 93.0 Mode :character Median : 1.0 Mode :character
Mean :104.6 Mean : 2.4
3rd Qu.:139.0 3rd Qu.: 3.0
Max. :810.0 Max. :3749.0
NA's :1570665 NA's :716340
BEGIN_LOCATION END_RANGE END_AZIMUTH END_LOCATION BEGIN_LAT
Length:1572907 Min. : 0.0 Length:1572907 Length:1572907 Min. :-14.4
Class :character 1st Qu.: 0.0 Class :character Class :character 1st Qu.: 34.4
Mode :character Median : 1.0 Mode :character Mode :character Median : 38.0
Mean : 2.3 Mean : 37.8
3rd Qu.: 3.0 3rd Qu.: 41.3
Max. :925.0 Max. : 70.5
NA's :716717 NA's :599776
BEGIN_LON END_LAT END_LON EPISODE_NARRATIVE EVENT_NARRATIVE
Min. :-171.0 Min. :-14.5 Min. :-815.1 Length:1572907 Length:1572907
1st Qu.: -97.5 1st Qu.: 34.6 1st Qu.: -97.5 Class :character Class :character
Median : -91.4 Median : 38.0 Median : -91.0 Mode :character Mode :character
Mean : -91.0 Mean : 37.9 Mean : -92.6
3rd Qu.: -83.5 3rd Qu.: 41.3 3rd Qu.: -83.2
Max. : 171.4 Max. : 70.4 Max. : 171.4
NA's :599776 NA's :777266 NA's :777265
DATA_SOURCE
Length:1572907
Class :character
Mode :character
length(unique(full$EVENT_TYPE))
[1] 74
Cleaning/Preparation
seperate BEGIN_YEARMONTH using a “-” and create new variable YMD that contains the year month and day seperated by a “-”
full$BEGIN_YEARMONTH <- gsub("(\\d{4})(\\d{2})", "\\1-\\2", full$BEGIN_YEARMONTH)
full$YMD <- paste(full$BEGIN_YEARMONTH, full$BEGIN_DAY, sep="-")
head(full)
Clean up DAMAGE_PROPERTY column so it can be used numerically and save as new variable
xin1 <- xin2 <- xin3 <- rep(NA,times=length(full$DAMAGE_PROPERTY))
for (i in 1:length(full$DAMAGE_PROPERTY)){
if (grepl("K",full$DAMAGE_PROPERTY[i])==T){
xin1[i] <- sub("K","",full$DAMAGE_PROPERTY[i])
}
if (grepl("M",full$DAMAGE_PROPERTY[i])==T){
xin2[i] <- sub("M","",full$DAMAGE_PROPERTY[i])
}
}
xin1 <- as.numeric(xin1)
xin2 <- as.numeric(xin2)
xin1 <- xin1 * 1000
xin2 <- xin2 * 1000000
xin1[is.na(xin1)] <- 0
xin2[is.na(xin2)] <- 0
xin3 <- xin1 + xin2
full$DAMAGE_PROPERTY_CLEAN <- xin3
head(full)
Adjust DAMAGE_PROPERTY_CLEAN column for inflation
# using quantmod package to access consumer price index data
getSymbols("CPIAUCSL", src='FRED')
‘getSymbols’ currently uses auto.assign=TRUE by default, but will
use auto.assign=FALSE in 0.5-0. You will still be able to use
‘loadSymbols’ to automatically load data. getOption("getSymbols.env")
and getOption("getSymbols.auto.assign") will still be checked for
alternate defaults.
This message is shown once per session and may be disabled by setting
options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.
[1] "CPIAUCSL"
cpi <- CPIAUCSL
cpiMonths <- substr(index(cpi), 1, 7)
cpidf <- data.frame("months" = cpiMonths, "cpi" = cpi)
names(cpidf) <- c("months", "CPI")
full <- merge(full, cpidf, by.x= "BEGIN_YEARMONTH", by.y= "months")
# our dataset has the most recent storm recorded being in july when the
# cpi was 255 so we are grabbing the cpi as of oct 1st (most recent for todays dollar)
cpitoday <- tail(cpi, 1)
full$multFactor <- as.vector(cpitoday) / full$CPI
full$ADJ_DAMAGE_PROPERTY <- full$DAMAGE_PROPERTY_CLEAN * full$multFactor
head(full)
Time Series Plots
Direct Deaths by Month
one outlier that was over 1000 removed
d_by_m <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=full )
names(d_by_m) <- c("BEGIN_YEARMONTH", "DEATHS_DIRECT")
windsor <- subset(d_by_m, DEATHS_DIRECT < 750)
n <- 20
numrow <- nrow(d_by_m)/n
seq1 <- seq(from=numrow, to=numrow*n-1, by=numrow)
ggplot(windsor, aes(BEGIN_YEARMONTH, DEATHS_DIRECT))+
geom_point(size=.8)+
scale_x_discrete(name="Year-Month", breaks=windsor$BEGIN_YEARMONTH[seq1])+
ggtitle("Directly Related Deaths by Month over 70 years", subtitle = "*one outlier removed")+
theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
ylab("Directly Related Deaths")

Damaged property per month
# aggregate deaths by year and month
d_by_m <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=full )
names(d_by_m) <- c("BEGIN_YEARMONTH", "ADJ_DAMAGE_PROPERTY")
# create a sequence for plotting a certain number (n) of discrete x variable dates, used to index year month in scale_x_discrete()
n <- 20
numrow <- nrow(d_by_m)/n
seq1 <- seq(from=numrow, to=numrow*n-1, by=numrow)
# PLOT!
ggplot(d_by_m, aes(BEGIN_YEARMONTH, ADJ_DAMAGE_PROPERTY))+
geom_point(size=.8)+
scale_x_discrete(name="Year-Month", breaks=d_by_m$BEGIN_YEARMONTH[seq1])+
ggtitle("Property Damage by Month over 70 years")+
theme(plot.title = element_text(hjust = 0.5))+
ylab("Adjusted for Inflation Damage to Property ($)")+
scale_y_continuous(label=comma)

Most dangerous storm deaths by day
here we can very clearly see that they started recording certain types of storms at different points in time
# select relevant columns
cols <- c("YMD", "DEATHS_DIRECT", "EVENT_TYPE", "YEAR")
work <- full[,cols]
# get most dangerous types of storms having DEATHS_DIRECT > third quartile
e_by_d <- aggregate(DEATHS_DIRECT ~ EVENT_TYPE, FUN = sum, data=full)
sub <- subset(e_by_d, DEATHS_DIRECT > 50)
# filter by most dangerous storm found above
# DEATHS_DIRECT < 100 removes one outlier that affects all of the graphs in the facet wrap so I removed it
new <- subset(work, EVENT_TYPE %in% sub$EVENT_TYPE & DEATHS_DIRECT < 100)
ggplot(new, aes(YMD, DEATHS_DIRECT))+
geom_point(size=.5)+
facet_wrap(vars(new$EVENT_TYPE))+
ggtitle("Deaths Over Time for Most Dangerous Types of Events")+
xlab("Year-Month-Day")+
theme_minimal()+
theme(plot.title = element_text(hjust = 0.5), axis.text.x=element_blank())

Frequency of storm events for every year in data set
saved as two seperate large pdfs for viewability
# used to get the count of each event type by year, from dplyr package
byYear <- full %>% count(YEAR, EVENT_TYPE)
# this is to limit the graph to storms that are most frequent using the first quartile
byYear <- subset(byYear, n > summary(byYear$n)[2])
seq1 <- 1950:1995
seq2 <- 1996:2019
firstSet <- byYear[byYear$YEAR %in% seq1,]
secondSet <- byYear[byYear$YEAR %in% seq2,]
# the second set contains so many types of events it had to be limited to storms that are most frequent using the first quartile
secondSet <- subset(secondSet, n > summary(secondSet$n)[2])
# PLOT!!!!
# 1953-1995
ggplot(firstSet, aes(EVENT_TYPE, n))+
geom_bar(stat='Identity')+
coord_flip()+
facet_wrap(vars(firstSet$YEAR))+
ggtitle("Frequency of Events by Year", subtitle = "1953-1995")+
theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
ylab("Frequency")+
xlab("Event Type")

ggsave("event_frequency_1953-1995.pdf")
Saving 30 x 16 in image
# 1996-2019
ggplot(secondSet, aes(EVENT_TYPE, n))+
geom_bar(stat='Identity')+
coord_flip()+
facet_wrap(vars(secondSet$YEAR))+
ggtitle("Frequency of Events by Year", subtitle = "1996-2019")+
theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
ylab("Frequency")+
xlab("Event Type")

ggsave("event_frequency_1996-2019.pdf")
Saving 30 x 16 in image
Summary plots
Most Costly Events in terms of property damage
subset based on the mean for viewability
e_by_d <- aggregate(ADJ_DAMAGE_PROPERTY ~ EVENT_TYPE, FUN = sum, data=full)
sub <- subset(e_by_d, ADJ_DAMAGE_PROPERTY > mean(e_by_d$ADJ_DAMAGE_PROPERTY))
ggplot(sub, aes(reorder(EVENT_TYPE, ADJ_DAMAGE_PROPERTY), ADJ_DAMAGE_PROPERTY))+
geom_bar(stat = 'identity')+
ggtitle("Most Costly Events in terms of Property Damage")+
theme(plot.title = element_text(hjust = 0.5))+
ylab("Property Damage Adjusted for Inflation ($)")+
xlab("Event Types")+
scale_y_continuous(label=comma)+
coord_flip()

Storm Events based on frequency
# obtain counts and subset out infrequent events using mean, again dplyr package
event <- full %>% count(EVENT_TYPE)
event <- subset(event, n >= mean(event$n))
# PLOT!!!!
ggplot(event, aes(reorder(EVENT_TYPE, n), n))+
geom_bar(stat='Identity')+
ggtitle("Frequency of Events for the Past 70 Years")+
theme(plot.title = element_text(hjust = 0.5))+
ylab("Frequency")+
xlab("Event Types")+
coord_flip()+
scale_y_continuous(labels = scales::comma) # forces ggplot to stop using scientific notation for y-axis

t.tests between 1996-2008 and 2009-2019 aggregated by Year-Month
NOAA only collected data on three types of severe storms until 1996 so in order to compare past data to more recent data we had to choose a date range that contained the same types of storms
Seperate data into two sets of years that contain similar types of events
yrs1 <- 1996:2007
yrs2 <- 2008:2019
early <- full[full$YEAR %in% yrs1,]
late <- full[full$YEAR %in% yrs2,]
d_early <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=early )
d_late <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=late )
i_early <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=early )
i_late <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=late )
p_early <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=early )
p_late <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=late )
# check that there is a similar amount in each subset of data, they all have the same # of rows
nrow(d_early)
[1] 144
nrow(d_late)
[1] 138
t.test of damaged property aggregated by year-month
H0 = there is no difference in damaged property between the two date ranges,
HA = there is a significant difference in damaged property between the two date ranges
hist(p_early$ADJ_DAMAGE_PROPERTY)

hist(p_late$ADJ_DAMAGE_PROPERTY)

t.test(p_early$ADJ_DAMAGE_PROPERTY, p_late$ADJ_DAMAGE_PROPERTY)
Welch Two Sample t-test
data: p_early$ADJ_DAMAGE_PROPERTY and p_late$ADJ_DAMAGE_PROPERTY
t = -0.56822, df = 230.01, p-value = 0.5704
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-553018314 305446901
sample estimates:
mean of x mean of y
849854710 973640417
t.test of damaged property, outliers removed
thresh <- 1000000000
p_earlyOut <- subset(early, ADJ_DAMAGE_PROPERTY < thresh)
p_lateOut<- subset(late, ADJ_DAMAGE_PROPERTY < thresh)
p_early <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=p_earlyOut )
p_late <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=p_lateOut )
summary(p_early$ADJ_DAMAGE_PROPERTY)
Min. 1st Qu. Median Mean 3rd Qu. Max.
18506625 235335400 495839318 826047326 925555247 10794085770
summary(p_late$ADJ_DAMAGE_PROPERTY)
Min. 1st Qu. Median Mean 3rd Qu. Max.
10750516 137834258 358143973 951391178 852358770 21480032375
hist(p_early$ADJ_DAMAGE_PROPERTY)

hist(p_late$ADJ_DAMAGE_PROPERTY)

t.test(p_early$ADJ_DAMAGE_PROPERTY, p_late$ADJ_DAMAGE_PROPERTY)
Welch Two Sample t-test
data: p_early$ADJ_DAMAGE_PROPERTY and p_late$ADJ_DAMAGE_PROPERTY
t = -0.5933, df = 214.52, p-value = 0.5536
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-541765914 291078209
sample estimates:
mean of x mean of y
826047326 951391178
t.tests of individual storms comparing directly related deaths, injuries, and property damage
H0 = there is no difference in directly related deaths, injuries, or property damage between the two date ranges
HA = there is a significant difference in directly related deaths, injuries, or property damage between the two date ranges
write a function to t-test a list of different storms removing outliers and aggregating by year-month
test_storms <- function(events){
return_list <- rep(NA, 1)
return_count <- 1
d_thresh <- 100
i_thresh <- 600
p_thresh <- 1000000000
earlyOut <- subset(early, ADJ_DAMAGE_PROPERTY < p_thresh & INJURIES_DIRECT < i_thresh & DEATHS_DIRECT < d_thresh)
lateOut<- subset(late, ADJ_DAMAGE_PROPERTY < p_thresh & INJURIES_DIRECT < i_thresh & DEATHS_DIRECT < d_thresh)
for (event in events){
eventEarly <- subset(earlyOut, EVENT_TYPE == event)
eventLate <- subset(lateOut, EVENT_TYPE == event)
d_eventEarly <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventEarly )
d_eventLate <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventLate )
i_eventEarly <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventEarly )
i_eventLate <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventLate )
p_eventEarly <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=eventEarly )
p_eventLate <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=eventLate )
# cannot reject null
d_test <- t.test(d_eventEarly$DEATHS_DIRECT, d_eventLate$DEATHS_DIRECT)
# cannot reject null
i_test <- t.test(i_eventEarly$INJURIES_DIRECT, i_eventLate$INJURIES_DIRECT)
# cannot reject null
p_test <- t.test(p_eventEarly$ADJ_DAMAGE_PROPERTY, p_eventLate$ADJ_DAMAGE_PROPERTY)
test_list <- list("event" = event, "deaths" = d_test, "injuries" = i_test, "prop_damage" = p_test)
return_list <- list.append(return_list, test_list)
}
return_list <- return_list[!is.na(return_list)]
return(return_list)
}
create list and use function
events <- c("Hurricane (Typhoon)", "Tornado", "Heat", "Flash Flood", "Flood",
"Thunderstorm Wind", "Hail", "High Wind", "Wildfire", "Lightning", "Rip Current")
test_storms(events)
$event
[1] "Hurricane (Typhoon)"
$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = 1.277, df = 42.471, p-value = 0.2085
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.413073 15.185683
sample estimates:
mean of x mean of y
6.4418605 0.5555556
$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 1.3553, df = 48.245, p-value = 0.1816
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.397831 27.733748
sample estimates:
mean of x mean of y
14.279070 3.111111
$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = 1.0191, df = 55.571, p-value = 0.3126
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-330496245 1014695271
sample estimates:
mean of x mean of y
609738096 267638584
[[5]]
[[5]]$event
[1] "Tornado"
[[5]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = -0.37366, df = 175.53, p-value = 0.7091
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.712206 3.893517
sample estimates:
mean of x mean of y
5.097902 6.007246
[[5]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 0.36112, df = 239.51, p-value = 0.7183
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-34.2825 49.6732
sample estimates:
mean of x mean of y
81.60839 73.91304
[[5]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = 0.12992, df = 248.58, p-value = 0.8967
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-65345667 74575637
sample estimates:
mean of x mean of y
110780089 106165104
[[6]]
[[6]]$event
[1] "Heat"
[[6]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = 1.8563, df = 124.06, p-value = 0.06578
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5658258 17.6478964
sample estimates:
mean of x mean of y
15.787611 7.246575
[[6]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 0.67914, df = 175.66, p-value = 0.4979
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-27.52565 56.40940
sample estimates:
mean of x mean of y
60.31858 45.87671
[[6]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = -0.6759, df = 126.58, p-value = 0.5003
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-285209.6 139982.8
sample estimates:
mean of x mean of y
82425.24 155038.60
[[7]]
[[7]]$event
[1] "Flash Flood"
[[7]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = -0.44784, df = 253.6, p-value = 0.6547
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.17306 1.36785
sample estimates:
mean of x mean of y
5.111888 5.514493
[[7]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 1.3673, df = 143.01, p-value = 0.1737
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-12.5941 69.1127
sample estimates:
mean of x mean of y
31.643357 3.384058
[[7]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = -0.97729, df = 142.32, p-value = 0.3301
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-382043311 129261216
sample estimates:
mean of x mean of y
115152263 241543310
[[8]]
[[8]]$event
[1] "Flood"
[[8]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = -2.1665, df = 263.37, p-value = 0.03117
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.54113369 -0.07360061
sample estimates:
mean of x mean of y
1.569444 2.376812
[[8]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 1.0787, df = 144.47, p-value = 0.2825
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.11674 37.82749
sample estimates:
mean of x mean of y
14.993056 1.637681
[[8]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = -1.3821, df = 209.88, p-value = 0.1684
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-109984729 19325393
sample estimates:
mean of x mean of y
83194105 128523773
[[9]]
[[9]]$event
[1] "Thunderstorm Wind"
[[9]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = -1.6684, df = 242.41, p-value = 0.09653
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.5018257 0.1244102
sample estimates:
mean of x mean of y
1.840278 2.528986
[[9]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 2.4659, df = 247.7, p-value = 0.01435
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
1.884546 16.842508
sample estimates:
mean of x mean of y
27.47222 18.10870
[[9]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = 0.22651, df = 240.31, p-value = 0.821
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-28333369 35695952
sample estimates:
mean of x mean of y
47547664 43866373
[[10]]
[[10]]$event
[1] "Hail"
[[10]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = 1.398, df = 187.57, p-value = 0.1638
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.01127332 0.06611922
sample estimates:
mean of x mean of y
0.03472222 0.00729927
[[10]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 1.5914, df = 203.64, p-value = 0.1131
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.4471504 4.1900539
sample estimates:
mean of x mean of y
3.944444 2.072993
[[10]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = -0.14622, df = 268.97, p-value = 0.8839
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-56047706 48298386
sample estimates:
mean of x mean of y
87044456 90919116
[[11]]
[[11]]$event
[1] "High Wind"
[[11]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = 1.7502, df = 233.79, p-value = 0.08139
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.07463717 1.26243911
sample estimates:
mean of x mean of y
1.5069444 0.9130435
[[11]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 3.9239, df = 268.28, p-value = 0.0001108
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
2.30633 6.95152
sample estimates:
mean of x mean of y
7.534722 2.905797
[[11]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = -0.29487, df = 245.2, p-value = 0.7683
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-62232001 46025257
sample estimates:
mean of x mean of y
32896616 40999988
[[12]]
[[12]]$event
[1] "Wildfire"
[[12]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = -1.0103, df = 154.84, p-value = 0.3139
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.3586744 0.7624178
sample estimates:
mean of x mean of y
0.6136364 1.4117647
[[12]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 2.2711, df = 167.6, p-value = 0.02441
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.6210173 8.8807652
sample estimates:
mean of x mean of y
9.530303 4.779412
[[12]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = 0.71503, df = 224.11, p-value = 0.4753
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-21668532 46348135
sample estimates:
mean of x mean of y
46091416 33751615
[[13]]
[[13]]$event
[1] "Lightning"
[[13]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = 3.0646, df = 255.51, p-value = 0.002413
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.5378441 2.4719256
sample estimates:
mean of x mean of y
3.762238 2.257353
[[13]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 3.7787, df = 237.17, p-value = 0.0001995
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
5.387296 17.123296
sample estimates:
mean of x mean of y
23.46853 12.21324
[[13]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = 2.2863, df = 259.18, p-value = 0.02304
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
235269.6 3156809.0
sample estimates:
mean of x mean of y
5173221 3477182
[[14]]
[[14]]$event
[1] "Rip Current"
[[14]]$deaths
Welch Two Sample t-test
data: d_eventEarly$DEATHS_DIRECT and d_eventLate$DEATHS_DIRECT
t = -3.1662, df = 221.47, p-value = 0.001762
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.3770092 -0.5531796
sample estimates:
mean of x mean of y
3.138211 4.603306
[[14]]$injuries
Welch Two Sample t-test
data: i_eventEarly$INJURIES_DIRECT and i_eventLate$INJURIES_DIRECT
t = 0.23818, df = 194.38, p-value = 0.812
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.301225 1.658680
sample estimates:
mean of x mean of y
3.186992 3.008264
[[14]]$prop_damage
Welch Two Sample t-test
data: p_eventEarly$ADJ_DAMAGE_PROPERTY and p_eventLate$ADJ_DAMAGE_PROPERTY
t = 1.4881, df = 122.02, p-value = 0.1393
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-619.1328 4368.6884
sample estimates:
mean of x mean of y
1884.679757 9.901952
t.tests of individual storms comparing frequencies
H0 = storms are not more or less frequent between the two date ranges
HA = storms are more frequent in one of the date ranges than the other
write a function to t-test a list of different storms aggregating by year-month
test_freq <- function(events){
return_list <- rep(NA,1)
for (event in events){
col_name <- paste(event, "_freq", sep="")
early[col_name] <- grepl(event, early$EVENT_TYPE)
late[col_name] <- grepl(event, late$EVENT_TYPE)
f_early <- aggregate(eval(as.name(col_name)) ~ BEGIN_YEARMONTH, FUN=sum, early)
f_late <- aggregate(eval(as.name(col_name)) ~ BEGIN_YEARMONTH, FUN=sum, late)
test_list <- list('event' = col_name, 'test' = t.test(f_early[,2], f_late[,2]))
return_list <- list.append(return_list, test_list)
}
return_list <- return_list[!is.na(return_list)]
return(return_list)
}
using the function to test list of storms except hurricanes
# there are two different ways of putting hurricane into the data so that has to be handled seperately
test_freq(events[2:length(events)])
$event
[1] "Tornado_freq"
$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -0.39215, df = 263.07, p-value = 0.6953
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-33.23437 22.19512
sample estimates:
mean of x mean of y
113.7847 119.3043
[[3]]
[[3]]$event
[1] "Heat_freq"
[[3]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -0.61382, df = 227.87, p-value = 0.5399
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-72.73975 38.18480
sample estimates:
mean of x mean of y
88.88194 106.15942
[[4]]
[[4]]$event
[1] "Flash Flood_freq"
[[4]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -1.6803, df = 250.39, p-value = 0.09415
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-108.105874 8.566019
sample estimates:
mean of x mean of y
270.5417 320.3116
[[5]]
[[5]]$event
[1] "Flood_freq"
[[5]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -2.9679, df = 248.24, p-value = 0.003292
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-201.0214 -40.6453
sample estimates:
mean of x mean of y
439.1667 560.0000
[[6]]
[[6]]$event
[1] "Thunderstorm Wind_freq"
[[6]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -2.7053, df = 242.34, p-value = 0.00731
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-727.2114 -114.4003
sample estimates:
mean of x mean of y
1046.035 1466.841
[[7]]
[[7]]$event
[1] "Hail_freq"
[[7]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = 0.41126, df = 278.71, p-value = 0.6812
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-199.0246 304.1484
sample estimates:
mean of x mean of y
1033.0764 980.5145
[[8]]
[[8]]$event
[1] "High Wind_freq"
[[8]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -1.3293, df = 277.24, p-value = 0.1849
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-85.16946 16.51004
sample estimates:
mean of x mean of y
228.0833 262.4130
[[9]]
[[9]]$event
[1] "Wildfire_freq"
[[9]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -2.9929, df = 268.48, p-value = 0.003021
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-15.125306 -3.121675
sample estimates:
mean of x mean of y
20.34028 29.46377
[[10]]
[[10]]$event
[1] "Lightning_freq"
[[10]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = 2.9215, df = 264.73, p-value = 0.003784
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
7.603057 39.034020
sample estimates:
mean of x mean of y
69.84028 46.52174
[[11]]
[[11]]$event
[1] "Rip Current_freq"
[[11]]$test
Welch Two Sample t-test
data: f_early[, 2] and f_late[, 2]
t = -3.5412, df = 234.31, p-value = 0.0004804
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-3.1329015 -0.8930647
sample estimates:
mean of x mean of y
3.631944 5.644928
testing hurricanes manually
event <- "Hurricane"
early$hurricane_freq <- grepl(event, early$EVENT_TYPE)
late$hurricane_freq <- grepl(event, late$EVENT_TYPE)
f_early <- aggregate(hurricane_freq ~ BEGIN_YEARMONTH, FUN=sum, early)
f_late <- aggregate(hurricane_freq ~ BEGIN_YEARMONTH, FUN=sum, late)
t.test(f_early$hurricane_freq, f_late$hurricane_freq)
Welch Two Sample t-test
data: f_early$hurricane_freq and f_late$hurricane_freq
t = 2.9066, df = 161.13, p-value = 0.004168
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
2.903162 15.208553
sample estimates:
mean of x mean of y
11.381944 2.326087
This code is looking at the three types of events we have consistently recorded throughout the 70 years: Hail, Thunderstorm Wind, and Tornadoes. The frequency of these events increase significantly in the later set of years from 1986-2019 but this is most likely due to better or more consistent recording practices by NOAA as opposed to these storms actually being that much more frequent.
yrs1 <- 1950:1985
yrs2 <- 1986:2019
early <- full[full$YEAR %in% yrs1,]
late <- full[full$YEAR %in% yrs2,]
early %>% count(EVENT_TYPE)
l <- late %>% count(EVENT_TYPE)
l[l$EVENT_TYPE %in% c("Hail", "Thunderstorm Wind", "Tornado"),]
Time Series Analysis
### animated times series graph over years
year <- as.numeric(substr(full$BEGIN_YEARMONTH, 1, 4))
head(year)
[1] 1950 1950 1950 1950 1950 1950
table(year) # data includes records from 1950 - 2019
year
1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
223 269 272 492 609 1413 1703 2184 2213 1813 1945 2246 2389 1968 2348 2835 2388
1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983
2688 3312 2926 3215 3471 2171 4453 5375 4975 3768 3728 3657 4279 6136 4517 7126 8322
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
7335 7979 8725 7363 7257 10407 10945 12516 13534 8664 15627 20461 48569 41983 50973 46383 52007
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
48875 50936 52956 52409 53976 56400 59010 71190 57398 62804 79091 64503 59985 59465 57788 56003 57024
2018 2019
62351 38586
max(year) - min(year) # equals 69, that's good!
[1] 69
full$year <- year
timess <- full %>% count(year) # matches the values from table function (line 45)
plot(timess, ylab = 'number of records', type = 'l', main = 'Time series for all event types from 1950-2019')
givemedf <- as.data.frame(table(year))
givemedf$year <- as.integer(as.numeric(levels(givemedf$year))[givemedf$year])
#givemedf$year <- as.Date(as.character(givemedf$year), "%Y")
ggplot(givemedf, aes(year, Freq)) + # animated time series plot for all event types
geom_line() +
geom_point(size = 2) +
transition_reveal(year) +
labs(title = "Year: {frame_along}", x = 'year', y = 'number of records') #+
[1] "./gganim_plot0001.png" "./gganim_plot0002.png" "./gganim_plot0003.png" "./gganim_plot0004.png"
[5] "./gganim_plot0005.png" "./gganim_plot0006.png" "./gganim_plot0007.png" "./gganim_plot0008.png"
[9] "./gganim_plot0009.png" "./gganim_plot0010.png" "./gganim_plot0011.png" "./gganim_plot0012.png"
[13] "./gganim_plot0013.png" "./gganim_plot0014.png" "./gganim_plot0015.png" "./gganim_plot0016.png"
[17] "./gganim_plot0017.png" "./gganim_plot0018.png" "./gganim_plot0019.png" "./gganim_plot0020.png"
[21] "./gganim_plot0021.png" "./gganim_plot0022.png" "./gganim_plot0023.png" "./gganim_plot0024.png"
[25] "./gganim_plot0025.png" "./gganim_plot0026.png" "./gganim_plot0027.png" "./gganim_plot0028.png"
[29] "./gganim_plot0029.png" "./gganim_plot0030.png" "./gganim_plot0031.png" "./gganim_plot0032.png"
[33] "./gganim_plot0033.png" "./gganim_plot0034.png" "./gganim_plot0035.png" "./gganim_plot0036.png"
[37] "./gganim_plot0037.png" "./gganim_plot0038.png" "./gganim_plot0039.png" "./gganim_plot0040.png"
[41] "./gganim_plot0041.png" "./gganim_plot0042.png" "./gganim_plot0043.png" "./gganim_plot0044.png"
[45] "./gganim_plot0045.png" "./gganim_plot0046.png" "./gganim_plot0047.png" "./gganim_plot0048.png"
[49] "./gganim_plot0049.png" "./gganim_plot0050.png" "./gganim_plot0051.png" "./gganim_plot0052.png"
[53] "./gganim_plot0053.png" "./gganim_plot0054.png" "./gganim_plot0055.png" "./gganim_plot0056.png"
[57] "./gganim_plot0057.png" "./gganim_plot0058.png" "./gganim_plot0059.png" "./gganim_plot0060.png"
[61] "./gganim_plot0061.png" "./gganim_plot0062.png" "./gganim_plot0063.png" "./gganim_plot0064.png"
[65] "./gganim_plot0065.png" "./gganim_plot0066.png" "./gganim_plot0067.png" "./gganim_plot0068.png"
[69] "./gganim_plot0069.png" "./gganim_plot0070.png" "./gganim_plot0071.png" "./gganim_plot0072.png"
[73] "./gganim_plot0073.png" "./gganim_plot0074.png" "./gganim_plot0075.png" "./gganim_plot0076.png"
[77] "./gganim_plot0077.png" "./gganim_plot0078.png" "./gganim_plot0079.png" "./gganim_plot0080.png"
[81] "./gganim_plot0081.png" "./gganim_plot0082.png" "./gganim_plot0083.png" "./gganim_plot0084.png"
[85] "./gganim_plot0085.png" "./gganim_plot0086.png" "./gganim_plot0087.png" "./gganim_plot0088.png"
[89] "./gganim_plot0089.png" "./gganim_plot0090.png" "./gganim_plot0091.png" "./gganim_plot0092.png"
[93] "./gganim_plot0093.png" "./gganim_plot0094.png" "./gganim_plot0095.png" "./gganim_plot0096.png"
[97] "./gganim_plot0097.png" "./gganim_plot0098.png" "./gganim_plot0099.png" "./gganim_plot0100.png"
attr(,"frame_vars")
frame nframes progress frame_along
1 1 100 0.01 1950
2 2 100 0.02 1951
3 3 100 0.03 1951
4 4 100 0.04 1952
5 5 100 0.05 1953
6 6 100 0.06 1953
7 7 100 0.07 1954
8 8 100 0.08 1955
9 9 100 0.09 1956
10 10 100 0.10 1956
11 11 100 0.11 1957
12 12 100 0.12 1958
13 13 100 0.13 1958
14 14 100 0.14 1959
15 15 100 0.15 1960
16 16 100 0.16 1960
17 17 100 0.17 1961
18 18 100 0.18 1962
19 19 100 0.19 1963
20 20 100 0.20 1963
21 21 100 0.21 1964
22 22 100 0.22 1965
23 23 100 0.23 1965
24 24 100 0.24 1966
25 25 100 0.25 1967
26 26 100 0.26 1967
27 27 100 0.27 1968
28 28 100 0.28 1969
29 29 100 0.29 1970
30 30 100 0.30 1970
31 31 100 0.31 1971
32 32 100 0.32 1972
33 33 100 0.33 1972
34 34 100 0.34 1973
35 35 100 0.35 1974
36 36 100 0.36 1974
37 37 100 0.37 1975
38 38 100 0.38 1976
39 39 100 0.39 1976
40 40 100 0.40 1977
41 41 100 0.41 1978
42 42 100 0.42 1979
43 43 100 0.43 1979
44 44 100 0.44 1980
45 45 100 0.45 1981
46 46 100 0.46 1981
47 47 100 0.47 1982
48 48 100 0.48 1983
49 49 100 0.49 1983
50 50 100 0.50 1984
51 51 100 0.51 1985
52 52 100 0.52 1986
53 53 100 0.53 1986
54 54 100 0.54 1987
55 55 100 0.55 1988
56 56 100 0.56 1988
57 57 100 0.57 1989
58 58 100 0.58 1990
59 59 100 0.59 1990
60 60 100 0.60 1991
61 61 100 0.61 1992
62 62 100 0.62 1993
63 63 100 0.63 1993
64 64 100 0.64 1994
65 65 100 0.65 1995
66 66 100 0.66 1995
67 67 100 0.67 1996
68 68 100 0.68 1997
69 69 100 0.69 1997
70 70 100 0.70 1998
71 71 100 0.71 1999
72 72 100 0.72 1999
73 73 100 0.73 2000
74 74 100 0.74 2001
75 75 100 0.75 2002
76 76 100 0.76 2002
77 77 100 0.77 2003
78 78 100 0.78 2004
79 79 100 0.79 2004
80 80 100 0.80 2005
81 81 100 0.81 2006
82 82 100 0.82 2006
83 83 100 0.83 2007
84 84 100 0.84 2008
85 85 100 0.85 2009
86 86 100 0.86 2009
87 87 100 0.87 2010
88 88 100 0.88 2011
89 89 100 0.89 2011
90 90 100 0.90 2012
91 91 100 0.91 2013
92 92 100 0.92 2013
93 93 100 0.93 2014
94 94 100 0.94 2015
95 95 100 0.95 2016
96 96 100 0.96 2016
97 97 100 0.97 2017
98 98 100 0.98 2018
99 99 100 0.99 2018
100 100 100 1.00 2019
frame_source
1 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0001.png
2 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0002.png
3 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0003.png
4 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0004.png
5 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0005.png
6 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0006.png
7 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0007.png
8 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0008.png
9 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0009.png
10 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0010.png
11 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0011.png
12 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0012.png
13 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0013.png
14 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0014.png
15 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0015.png
16 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0016.png
17 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0017.png
18 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0018.png
19 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0019.png
20 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0020.png
21 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0021.png
22 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0022.png
23 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0023.png
24 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0024.png
25 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0025.png
26 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0026.png
27 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0027.png
28 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0028.png
29 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0029.png
30 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0030.png
31 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0031.png
32 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0032.png
33 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0033.png
34 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0034.png
35 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0035.png
36 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0036.png
37 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0037.png
38 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0038.png
39 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0039.png
40 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0040.png
41 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0041.png
42 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0042.png
43 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0043.png
44 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0044.png
45 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0045.png
46 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0046.png
47 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0047.png
48 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0048.png
49 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0049.png
50 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0050.png
51 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0051.png
52 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0052.png
53 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0053.png
54 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0054.png
55 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0055.png
56 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0056.png
57 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0057.png
58 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0058.png
59 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0059.png
60 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0060.png
61 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0061.png
62 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0062.png
63 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0063.png
64 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0064.png
65 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0065.png
66 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0066.png
67 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0067.png
68 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0068.png
69 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0069.png
70 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0070.png
71 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0071.png
72 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0072.png
73 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0073.png
74 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0074.png
75 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0075.png
76 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0076.png
77 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0077.png
78 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0078.png
79 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0079.png
80 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0080.png
81 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0081.png
82 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0082.png
83 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0083.png
84 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0084.png
85 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0085.png
86 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0086.png
87 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0087.png
88 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0088.png
89 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0089.png
90 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0090.png
91 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0091.png
92 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0092.png
93 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0093.png
94 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0094.png
95 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0095.png
96 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0096.png
97 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0097.png
98 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0098.png
99 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0099.png
100 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da773ef3a526/gganim_plot0100.png
#anim_save('all_event_new.gif') # Comment this line if you don't want to save it as a gif locally
fancy <- as.data.frame(head(sort(table(full$EVENT_TYPE)[table(full$EVENT_TYPE) > 10000],decreasing = T),9))
colnames(fancy)[1] <- "event_type"
fancy # Top 9 event types with most records
flag <- fancy[,1]
fullflag <- full[full$EVENT_TYPE==flag,]
longer object length is not a multiple of shorter object lengthlonger object length is not a multiple of shorter object length
table(fullflag$EVENT_TYPE,fullflag$year) # 6 event types with 0 records until 1995 (Drought, Flash Flood, HS, HW, WS, WW)
1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
Drought 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Flash Flood 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hail 0 0 0 0 0 39 47 56 74 56 62 87 103 70 82 88 88
Heavy Snow 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
High Wind 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thunderstorm Wind 0 0 0 0 0 42 89 74 79 71 78 86 88 88 93 108 111
Tornado 25 30 30 55 67 75 67 102 61 57 71 87 71 64 80 113 63
Winter Storm 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Winter Weather 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983
Drought 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Flash Flood 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hail 83 113 85 79 99 73 110 184 154 128 110 114 151 213 164 285 260
Heavy Snow 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
High Wind 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Thunderstorm Wind 95 175 173 194 167 77 224 293 305 184 189 210 241 344 241 400 564
Tornado 106 81 77 76 116 87 123 128 103 109 98 99 108 110 93 120 103
Winter Storm 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Winter Weather 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Drought 0 0 0 0 0 0 0 0 0 0 0 0 72 45 192 285 254
Flash Flood 0 0 0 0 0 0 0 0 0 0 0 0 372 314 401 295 302
Hail 316 392 403 289 287 428 409 509 651 462 755 932 1219 952 1402 1127 1233
Heavy Snow 0 0 0 0 0 0 0 0 0 0 0 0 480 346 258 279 444
High Wind 0 0 0 0 0 0 0 0 0 0 0 0 415 301 248 354 332
Thunderstorm Wind 388 443 444 466 455 637 693 733 741 447 853 1226 1137 1094 1520 1118 1349
Tornado 110 98 85 86 92 102 147 145 157 74 90 153 145 123 141 189 131
Winter Storm 0 0 0 0 0 0 0 0 0 0 0 0 351 357 218 336 458
Winter Weather 0 0 0 0 0 0 0 0 0 0 0 0 72 124 89 130 51
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Drought 199 218 111 60 170 290 398 216 158 175 614 878 584 301 149 267 136
Flash Flood 327 360 454 533 329 297 412 430 444 447 396 257 530 321 566 417 386
Hail 1286 1373 1542 1475 1498 1844 1401 1965 1483 1241 1938 1496 1045 1120 1025 1034 1148
Heavy Snow 315 371 318 240 305 170 285 309 256 397 305 159 252 305 292 156 193
High Wind 320 354 244 268 264 308 251 413 323 311 354 379 247 346 230 350 402
Thunderstorm Wind 1311 1321 1378 1332 1307 1490 1468 1861 1445 1749 2324 1795 1660 1546 1588 1746 1830
Tornado 133 137 168 213 153 134 135 212 166 157 242 119 117 111 124 104 193
Winter Storm 278 373 402 351 334 215 346 457 373 424 352 196 415 437 402 259 241
Winter Weather 83 42 192 182 222 171 376 447 312 478 417 308 531 534 460 304 285
2018 2019
Drought 273 17
Flash Flood 474 229
Hail 901 602
Heavy Snow 239 218
High Wind 328 256
Thunderstorm Wind 1669 1102
Tornado 131 148
Winter Storm 397 241
Winter Weather 491 269
givemedf2 <- as.data.frame(table(fullflag$EVENT_TYPE,fullflag$year))
givemedf2$Var2 <- as.integer(as.numeric(levels(givemedf2$Var2))[givemedf2$Var2])
#givemedf2$Var2 <- as.Date(as.character(givemedf2$Var2), "%Y")
ggplot(givemedf2, aes(Var2, Freq, group=Var1)) + # animated time series plot for top 9 event types with most records
geom_line(aes(col = Var1), size = 1) +
scale_colour_discrete("event type") +
geom_point(size = 2) +
transition_reveal(Var2) +
labs(title = "Year: {frame_along}", x = 'year', y = 'number of records') #+
[1] "./gganim_plot0001.png" "./gganim_plot0002.png" "./gganim_plot0003.png" "./gganim_plot0004.png"
[5] "./gganim_plot0005.png" "./gganim_plot0006.png" "./gganim_plot0007.png" "./gganim_plot0008.png"
[9] "./gganim_plot0009.png" "./gganim_plot0010.png" "./gganim_plot0011.png" "./gganim_plot0012.png"
[13] "./gganim_plot0013.png" "./gganim_plot0014.png" "./gganim_plot0015.png" "./gganim_plot0016.png"
[17] "./gganim_plot0017.png" "./gganim_plot0018.png" "./gganim_plot0019.png" "./gganim_plot0020.png"
[21] "./gganim_plot0021.png" "./gganim_plot0022.png" "./gganim_plot0023.png" "./gganim_plot0024.png"
[25] "./gganim_plot0025.png" "./gganim_plot0026.png" "./gganim_plot0027.png" "./gganim_plot0028.png"
[29] "./gganim_plot0029.png" "./gganim_plot0030.png" "./gganim_plot0031.png" "./gganim_plot0032.png"
[33] "./gganim_plot0033.png" "./gganim_plot0034.png" "./gganim_plot0035.png" "./gganim_plot0036.png"
[37] "./gganim_plot0037.png" "./gganim_plot0038.png" "./gganim_plot0039.png" "./gganim_plot0040.png"
[41] "./gganim_plot0041.png" "./gganim_plot0042.png" "./gganim_plot0043.png" "./gganim_plot0044.png"
[45] "./gganim_plot0045.png" "./gganim_plot0046.png" "./gganim_plot0047.png" "./gganim_plot0048.png"
[49] "./gganim_plot0049.png" "./gganim_plot0050.png" "./gganim_plot0051.png" "./gganim_plot0052.png"
[53] "./gganim_plot0053.png" "./gganim_plot0054.png" "./gganim_plot0055.png" "./gganim_plot0056.png"
[57] "./gganim_plot0057.png" "./gganim_plot0058.png" "./gganim_plot0059.png" "./gganim_plot0060.png"
[61] "./gganim_plot0061.png" "./gganim_plot0062.png" "./gganim_plot0063.png" "./gganim_plot0064.png"
[65] "./gganim_plot0065.png" "./gganim_plot0066.png" "./gganim_plot0067.png" "./gganim_plot0068.png"
[69] "./gganim_plot0069.png" "./gganim_plot0070.png" "./gganim_plot0071.png" "./gganim_plot0072.png"
[73] "./gganim_plot0073.png" "./gganim_plot0074.png" "./gganim_plot0075.png" "./gganim_plot0076.png"
[77] "./gganim_plot0077.png" "./gganim_plot0078.png" "./gganim_plot0079.png" "./gganim_plot0080.png"
[81] "./gganim_plot0081.png" "./gganim_plot0082.png" "./gganim_plot0083.png" "./gganim_plot0084.png"
[85] "./gganim_plot0085.png" "./gganim_plot0086.png" "./gganim_plot0087.png" "./gganim_plot0088.png"
[89] "./gganim_plot0089.png" "./gganim_plot0090.png" "./gganim_plot0091.png" "./gganim_plot0092.png"
[93] "./gganim_plot0093.png" "./gganim_plot0094.png" "./gganim_plot0095.png" "./gganim_plot0096.png"
[97] "./gganim_plot0097.png" "./gganim_plot0098.png" "./gganim_plot0099.png" "./gganim_plot0100.png"
attr(,"frame_vars")
frame nframes progress frame_along
1 1 100 0.01 1950
2 2 100 0.02 1951
3 3 100 0.03 1951
4 4 100 0.04 1952
5 5 100 0.05 1953
6 6 100 0.06 1953
7 7 100 0.07 1954
8 8 100 0.08 1955
9 9 100 0.09 1956
10 10 100 0.10 1956
11 11 100 0.11 1957
12 12 100 0.12 1958
13 13 100 0.13 1958
14 14 100 0.14 1959
15 15 100 0.15 1960
16 16 100 0.16 1960
17 17 100 0.17 1961
18 18 100 0.18 1962
19 19 100 0.19 1963
20 20 100 0.20 1963
21 21 100 0.21 1964
22 22 100 0.22 1965
23 23 100 0.23 1965
24 24 100 0.24 1966
25 25 100 0.25 1967
26 26 100 0.26 1967
27 27 100 0.27 1968
28 28 100 0.28 1969
29 29 100 0.29 1970
30 30 100 0.30 1970
31 31 100 0.31 1971
32 32 100 0.32 1972
33 33 100 0.33 1972
34 34 100 0.34 1973
35 35 100 0.35 1974
36 36 100 0.36 1974
37 37 100 0.37 1975
38 38 100 0.38 1976
39 39 100 0.39 1976
40 40 100 0.40 1977
41 41 100 0.41 1978
42 42 100 0.42 1979
43 43 100 0.43 1979
44 44 100 0.44 1980
45 45 100 0.45 1981
46 46 100 0.46 1981
47 47 100 0.47 1982
48 48 100 0.48 1983
49 49 100 0.49 1983
50 50 100 0.50 1984
51 51 100 0.51 1985
52 52 100 0.52 1986
53 53 100 0.53 1986
54 54 100 0.54 1987
55 55 100 0.55 1988
56 56 100 0.56 1988
57 57 100 0.57 1989
58 58 100 0.58 1990
59 59 100 0.59 1990
60 60 100 0.60 1991
61 61 100 0.61 1992
62 62 100 0.62 1993
63 63 100 0.63 1993
64 64 100 0.64 1994
65 65 100 0.65 1995
66 66 100 0.66 1995
67 67 100 0.67 1996
68 68 100 0.68 1997
69 69 100 0.69 1997
70 70 100 0.70 1998
71 71 100 0.71 1999
72 72 100 0.72 1999
73 73 100 0.73 2000
74 74 100 0.74 2001
75 75 100 0.75 2002
76 76 100 0.76 2002
77 77 100 0.77 2003
78 78 100 0.78 2004
79 79 100 0.79 2004
80 80 100 0.80 2005
81 81 100 0.81 2006
82 82 100 0.82 2006
83 83 100 0.83 2007
84 84 100 0.84 2008
85 85 100 0.85 2009
86 86 100 0.86 2009
87 87 100 0.87 2010
88 88 100 0.88 2011
89 89 100 0.89 2011
90 90 100 0.90 2012
91 91 100 0.91 2013
92 92 100 0.92 2013
93 93 100 0.93 2014
94 94 100 0.94 2015
95 95 100 0.95 2016
96 96 100 0.96 2016
97 97 100 0.97 2017
98 98 100 0.98 2018
99 99 100 0.99 2018
100 100 100 1.00 2019
frame_source
1 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0001.png
2 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0002.png
3 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0003.png
4 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0004.png
5 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0005.png
6 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0006.png
7 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0007.png
8 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0008.png
9 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0009.png
10 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0010.png
11 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0011.png
12 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0012.png
13 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0013.png
14 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0014.png
15 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0015.png
16 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0016.png
17 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0017.png
18 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0018.png
19 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0019.png
20 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0020.png
21 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0021.png
22 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0022.png
23 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0023.png
24 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0024.png
25 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0025.png
26 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0026.png
27 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0027.png
28 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0028.png
29 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0029.png
30 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0030.png
31 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0031.png
32 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0032.png
33 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0033.png
34 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0034.png
35 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0035.png
36 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0036.png
37 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0037.png
38 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0038.png
39 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0039.png
40 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0040.png
41 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0041.png
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43 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0043.png
44 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0044.png
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46 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0046.png
47 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0047.png
48 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0048.png
49 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0049.png
50 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0050.png
51 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0051.png
52 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0052.png
53 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0053.png
54 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0054.png
55 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0055.png
56 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0056.png
57 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0057.png
58 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0058.png
59 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0059.png
60 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0060.png
61 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0061.png
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70 /var/folders/n2/lc639sxd11v3ckv2rbnwxt8r0000gn/T//RtmpQsQnnu/da77d37966b/gganim_plot0070.png
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#anim_save('top9_new.gif') # Comment this line if you don't want to save it as a gif locally
### severe weather in Colorado predictive model
full2 <- full[(full$STATE=='COLORADO') & (full$year != 2019),] # subset all Colorado records & get rid of 2019 (since no year-long data)
fancy <- as.data.frame(head(sort(table(full2$EVENT_TYPE),decreasing = T),9)) # Top 9 most happened severe weather in Colorado
colnames(fancy)[1] <- "event_type" # Hail, Winter Storm, Winter Weather, Thunderstorm...
fancy
timess2 <- full2 %>% count(year)
plot(timess2, ylab = 'number of records', type = 'l', main = 'Time series for all event types happened in Colorado from 1950-2018') # Time series plot for all severe weather types in Colorado
par(mfrow = c(3,3))

arnumber <- rep(NA, times=nrow(fancy))
pred19 <- rep(NA, times=nrow(fancy))
pred20 <- rep(NA, times=nrow(fancy))
for (i in 1:nrow(fancy)){
fullsub <- full2[full2$EVENT_TYPE==fancy[i,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[i,1]))
ar1 <- ar(newtimess[,2])
arnumber[i] <- ar1$order
predRe <- predict(ar1,n.ahead = 2)
pred19[i] <- predRe$pred[1]
pred20[i] <- predRe$pred[2]
}
#################### Example of Hail (Colorado)
par(mfrow = c(1,1))

fullsub <- full2[full2$EVENT_TYPE==fancy[1,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[1,1]))
par(mfrow = c(1,2))

acf(newtimess[,2])
pacf(newtimess[,2])
## Since pacf displays a sharp cutoff and acf displays a slow decay, this series displays an "ar signature"
## by reading both pacf and acf, we are confident to use ar(15) model since PACF cut off is at lag 15.
#################### Example of Drought (Colorado)
par(mfrow = c(1,1))

fullsub <- full2[full2$EVENT_TYPE==fancy[6,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[6,1]))
par(mfrow = c(1,2))

acf(newtimess[,2])
pacf(newtimess[,2])

## We can see that for Drought, there is no strong geometry decay pattern for both acf and pacf, so it's not a good idea
## to use ar function on Drought (similar situation for High Wind, Flash Flood). It's also interesting for ar model of
## this type of weather, the automatically selected order window is 0. And the 2019 prediction ^ 2020 prediction are the same.
arnumber_new <- arnumber
pred19_new <- floor(pred19)
pred20_new <- floor(pred20)
fancytype <- as.character(fancy[,1])
givemetable <- cbind(fancytype, arnumber_new, pred19_new, pred20_new)
colnames(givemetable)[1] <- "Event Type"
colnames(givemetable)[2] <- "Lag Window"
colnames(givemetable)[3] <- "2019 Prediction"
colnames(givemetable)[4] <- "2020 Prediction"
xtable(givemetable)
% latex table generated in R 3.6.1 by xtable 1.8-4 package
% Sat Nov 23 17:29:40 2019
\begin{table}[ht]
\centering
\begin{tabular}{rllll}
\hline
& Event Type & Lag Window & 2019 Prediction & 2020 Prediction \\
\hline
1 & Hail & 2 & 468 & 485 \\
2 & Winter Storm & 1 & 148 & 179 \\
3 & Winter Weather & 1 & 274 & 262 \\
4 & Thunderstorm Wind & 6 & 76 & 87 \\
5 & High Wind & 0 & 110 & 110 \\
6 & Drought & 0 & 145 & 145 \\
7 & Tornado & 8 & 40 & 41 \\
8 & Heavy Snow & 1 & 44 & 60 \\
9 & Flash Flood & 0 & 51 & 51 \\
\hline
\end{tabular}
\end{table}
Predictive Modelling
### severe weather in Colorado predictive model
full2 <- full[(full$STATE=='COLORADO') & (full$year != 2019),] # subset all Colorado records & get rid of 2019 (since no year-long data)
fancy <- as.data.frame(head(sort(table(full2$EVENT_TYPE),decreasing = T),9)) # Top 9 most happened severe weather in Colorado
colnames(fancy)[1] <- "event_type" # Hail, Winter Storm, Winter Weather, Thunderstorm...
fancy
timess2 <- full2 %>% count(year)
plot(timess2, ylab = 'number of records', type = 'l', main = 'Time series for all event types happened in Colorado from 1950-2018') # Time series plot for all severe weather types in Colorado
par(mfrow = c(3,3))

arnumber <- rep(NA, times=nrow(fancy))
pred19 <- rep(NA, times=nrow(fancy))
pred20 <- rep(NA, times=nrow(fancy))
for (i in 1:nrow(fancy)){
fullsub <- full2[full2$EVENT_TYPE==fancy[i,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[i,1]))
ar1 <- ar(newtimess[,2])
arnumber[i] <- ar1$order
predRe <- predict(ar1,n.ahead = 2)
pred19[i] <- predRe$pred[1]
pred20[i] <- predRe$pred[2]
}

#################### Example of Hail (Colorado)
par(mfrow = c(1,1))
fullsub <- full2[full2$EVENT_TYPE==fancy[1,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[1,1]))
par(mfrow = c(1,2))

acf(newtimess[,2])
pacf(newtimess[,2])

## Since pacf displays a sharp cutoff and acf displays a slow decay, this series displays an "ar signature"
## by reading both pacf and acf, we are confident to use ar(15) model since PACF cut off is at lag 15.
#################### Example of Drought (Colorado)
par(mfrow = c(1,1))
fullsub <- full2[full2$EVENT_TYPE==fancy[6,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[6,1]))
par(mfrow = c(1,2))

acf(newtimess[,2])
pacf(newtimess[,2])

## We can see that for Drought, there is no strong geometry decay pattern for both acf and pacf, so it's not a good idea
## to use ar function on Drought (similar situation for High Wind, Flash Flood). It's also interesting for ar model of
## this type of weather, the automatically selected order window is 0. And the 2019 prediction ^ 2020 prediction are the same.
arnumber_new <- arnumber
pred19_new <- floor(pred19)
pred20_new <- floor(pred20)
fancytype <- as.character(fancy[,1])
givemetable <- cbind(fancytype, arnumber_new, pred19_new, pred20_new)
colnames(givemetable)[1] <- "Event Type"
colnames(givemetable)[2] <- "Lag Window"
colnames(givemetable)[3] <- "2019 Prediction"
colnames(givemetable)[4] <- "2020 Prediction"
xtable(givemetable)
% latex table generated in R 3.6.1 by xtable 1.8-4 package
% Sat Nov 23 17:33:42 2019
\begin{table}[ht]
\centering
\begin{tabular}{rllll}
\hline
& Event Type & Lag Window & 2019 Prediction & 2020 Prediction \\
\hline
1 & Hail & 2 & 468 & 485 \\
2 & Winter Storm & 1 & 148 & 179 \\
3 & Winter Weather & 1 & 274 & 262 \\
4 & Thunderstorm Wind & 6 & 76 & 87 \\
5 & High Wind & 0 & 110 & 110 \\
6 & Drought & 0 & 145 & 145 \\
7 & Tornado & 8 & 40 & 41 \\
8 & Heavy Snow & 1 & 44 & 60 \\
9 & Flash Flood & 0 & 51 & 51 \\
\hline
\end{tabular}
\end{table}
Creating the fullWeather.csv file from the downloaded NOAA files
Rename files in details folder to allow for binding of data (only needed to be performed once)
Bind files from every year together into dataframe labelled “full” (70 years worth of data!) then export to csv file for ease of use and sharing purposes (only needed to be performed once)
---
title: "Severe Weather Data from NOAA 1950-2019"
output:
  html_notebook
---
## by: Kaegan Casey, Patrick Durkin, Mengchao Li, Yongbo Shu, Yiwei Yan
### data: [https://www1.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/](https://www1.ncdc.noaa.gov/pub/data/swdi/stormevents/csvfiles/)

***
# <a name="nav"></a>
# Navigation:
###### Introduction:
1. [Import Libraries and read in data](#import)
2. [Descriptive Statistics](#desstat)
3. [Cleaning/Preparation](#clean)

###### Visualizations:
4. [Time Series Plots](#time)
5. [Summary Plots](#sum)

###### Conclusions:
6. [T-tests](#t1)
    + [Overall](#t1)
    + [Individual Storms](#t2)
7. [Time Series Analysis](#timeanal)
8. [Predictive Modelling](#predict)

###### Extras:
7. [Creating our csv](#csv)

*** 
# <a name="import"></a>
###### [back to navigation](#nav)
# Import libraries and read in data 
```{r}
library(ggplot2)
library(plyr)
library(dplyr)
library(quantmod)
library(scales)
library(rlist)
library(tidyverse)
library(transformr)
library(gganimate)
library(xtable)

options(stringsAsFactors = FALSE, scipen=99999)

# weatherFull just needs to be in the same folder as this notebook .rmb file
full <- read.csv('weatherFull.csv')
```

***
# <a name="desstat"></a>
###### [back to navigation](#nav)
# Descriptive Statistics
```{r}
head(full)
dim(full)
summary(full[,1:26])
summary(full[,27:52])
length(unique(full$EVENT_TYPE))
```

***
# <a name="clean"></a>
###### [back to navigation](#nav)
# Cleaning/Preparation
### seperate BEGIN_YEARMONTH using a "-" and create new variable YMD that contains the year month and day seperated by a "-"
```{r}
full$BEGIN_YEARMONTH <- gsub("(\\d{4})(\\d{2})", "\\1-\\2", full$BEGIN_YEARMONTH)
full$YMD <- paste(full$BEGIN_YEARMONTH, full$BEGIN_DAY, sep="-")
head(full)
```

### Clean up DAMAGE_PROPERTY column so it can be used numerically and save as new variable
```{r}
xin1 <- xin2 <- xin3 <- rep(NA,times=length(full$DAMAGE_PROPERTY))


for (i in 1:length(full$DAMAGE_PROPERTY)){
  if (grepl("K",full$DAMAGE_PROPERTY[i])==T){
    xin1[i] <- sub("K","",full$DAMAGE_PROPERTY[i])
  }
  if (grepl("M",full$DAMAGE_PROPERTY[i])==T){
    xin2[i] <- sub("M","",full$DAMAGE_PROPERTY[i])
  }
}
xin1 <- as.numeric(xin1)
xin2 <- as.numeric(xin2)
xin1 <- xin1 * 1000
xin2 <- xin2 * 1000000
xin1[is.na(xin1)] <- 0
xin2[is.na(xin2)] <- 0

xin3 <- xin1 + xin2
full$DAMAGE_PROPERTY_CLEAN <- xin3
head(full)
```

### Adjust DAMAGE_PROPERTY_CLEAN column for inflation
```{r}
#   using quantmod package to access consumer price index data
getSymbols("CPIAUCSL", src='FRED')
cpi <- CPIAUCSL

cpiMonths <- substr(index(cpi), 1, 7)

cpidf <- data.frame("months" = cpiMonths, "cpi" = cpi)
names(cpidf) <- c("months", "CPI")

full <- merge(full, cpidf, by.x= "BEGIN_YEARMONTH", by.y= "months")

# our dataset has the most recent storm recorded being in july when the 
# cpi was 255 so we are grabbing the cpi as of oct 1st (most recent for todays dollar)
cpitoday <- tail(cpi, 1)

full$multFactor <- as.vector(cpitoday) / full$CPI 

full$ADJ_DAMAGE_PROPERTY <- full$DAMAGE_PROPERTY_CLEAN * full$multFactor
head(full)
```

***
# <a name="time"></a>
###### [back to navigation](#nav)
# Time Series Plots
### Direct Deaths by Month
##### one outlier that was over 1000 removed
```{r fig.width=8, fig.height=4}
d_by_m <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=full )
names(d_by_m) <- c("BEGIN_YEARMONTH", "DEATHS_DIRECT")

windsor <- subset(d_by_m, DEATHS_DIRECT < 750)

n <- 20
numrow <- nrow(d_by_m)/n
seq1 <- seq(from=numrow, to=numrow*n-1, by=numrow)

ggplot(windsor, aes(BEGIN_YEARMONTH, DEATHS_DIRECT))+
  geom_point(size=.8)+
  scale_x_discrete(name="Year-Month", breaks=windsor$BEGIN_YEARMONTH[seq1])+
  ggtitle("Directly Related Deaths by Month over 70 years", subtitle = "*one outlier removed")+
  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
  ylab("Directly Related Deaths")
```

### Directly related injuries per month
```{r fig.width=8, fig.height=4}
d_by_m <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=full )
names(d_by_m) <- c("BEGIN_YEARMONTH", "INJURIES_DIRECT")

#   create a sequence for plotting a certain number (n) of discrete x variable dates, used to index year month in scale_x_discrete()
n <- 20
numrow <- nrow(d_by_m)/n
seq1 <- seq(from=numrow, to=numrow*n-1, by=numrow)

#   PLOT!
ggplot(d_by_m, aes(BEGIN_YEARMONTH, INJURIES_DIRECT))+
  geom_point(size=.8)+
  scale_x_discrete(name="Year-Month", breaks=d_by_m$BEGIN_YEARMONTH[seq1])+
  ggtitle("Directly Related Injuries by Month over 70 years")+
  theme(plot.title = element_text(hjust = 0.5))+
  ylab("Directly Related Injuries")
```


### Damaged property per month
```{r fig.width=8, fig.height=4}
#   aggregate deaths by year and month
d_by_m <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=full )
names(d_by_m) <- c("BEGIN_YEARMONTH", "ADJ_DAMAGE_PROPERTY")

#   create a sequence for plotting a certain number (n) of discrete x variable dates, used to index year month in scale_x_discrete()
n <- 20
numrow <- nrow(d_by_m)/n
seq1 <- seq(from=numrow, to=numrow*n-1, by=numrow)

#   PLOT!
ggplot(d_by_m, aes(BEGIN_YEARMONTH, ADJ_DAMAGE_PROPERTY))+
  geom_point(size=.8)+
  scale_x_discrete(name="Year-Month", breaks=d_by_m$BEGIN_YEARMONTH[seq1])+
  ggtitle("Property Damage by Month over 70 years")+
  theme(plot.title = element_text(hjust = 0.5))+
  ylab("Adjusted for Inflation Damage to Property ($)")+
  scale_y_continuous(label=comma)
```

### Most dangerous storm deaths by day
##### here we can very clearly see that they started recording certain types of storms at different points in time
```{r fig.width=10, fig.height=6}
#   select relevant columns
cols <- c("YMD", "DEATHS_DIRECT", "EVENT_TYPE", "YEAR")
work <- full[,cols]

#   get most dangerous types of storms having DEATHS_DIRECT > third quartile
e_by_d <- aggregate(DEATHS_DIRECT ~ EVENT_TYPE, FUN = sum, data=full)
sub <- subset(e_by_d, DEATHS_DIRECT > 50) 

#   filter by most dangerous storm found above
#   DEATHS_DIRECT < 100 removes one outlier that affects all of the graphs in the facet wrap so I removed it
new <- subset(work, EVENT_TYPE %in% sub$EVENT_TYPE & DEATHS_DIRECT < 100)   

ggplot(new, aes(YMD, DEATHS_DIRECT))+
  geom_point(size=.5)+
  facet_wrap(vars(new$EVENT_TYPE))+
  ggtitle("Deaths Over Time for Most Dangerous Types of Events")+
  xlab("Year-Month-Day")+
  theme_minimal()+
  theme(plot.title = element_text(hjust = 0.5), axis.text.x=element_blank())
```

### Barplots of directly related deaths by storm type for every year in the data set
##### saved as two seperate large pdfs for viewability
```{r fig.width=15, fig.height=8}
#   aggregate full dataset by year and event type finding the sum of directly related deaths
agg <- aggregate(DEATHS_DIRECT ~ YEAR + EVENT_TYPE, FUN=sum, data=full)
agg <- agg[order(agg$YEAR),]

#   create year sequences
seq1 <- 1950:1995
seq2 <- 1996:2019

firstSet <- agg[agg$YEAR %in% seq1,]
secondSet <- agg[agg$YEAR %in% seq2,]

#   Since a bunch of new EVENT_TYPES were added around 1955 there were a lot of extra ones that have 0 direct deaths associated with them so they were filtered out for viewing purposes.
firstSet <- subset(firstSet, DEATHS_DIRECT > 0)

#   this is based off of the 3rd qurtile of DEATHS_DIRECT and is meant to narrow it down to more dangerous types of events because there are so many types of events it is for viewing purposes.
secondSet <- subset(secondSet, DEATHS_DIRECT > summary(secondSet$DEATHS_DIRECT)[5])


#   PLOT!!!!
#   1950-1995
ggplot(firstSet, aes(EVENT_TYPE, DEATHS_DIRECT))+
  geom_bar(stat = 'identity')+
  coord_flip()+
  facet_wrap(vars(firstSet$YEAR))+
  ggtitle("Directly Related Deaths by Event Type", subtitle = "1950-1995")+
  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
  xlab("Directly Related Deaths")+
  ylab("Event Type")


ggsave('deaths_by_EVENT_TYPE_1950-1995.pdf')

#   1996-2019
 ggplot(secondSet, aes(EVENT_TYPE, DEATHS_DIRECT))+
  geom_bar(stat = 'identity')+
  coord_flip()+
  facet_wrap(vars(secondSet$YEAR))+
  ggtitle("Directly Related Deaths by Event Type", subtitle = "1996-2019")+
  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
  xlab("Directly Related Deaths")+
  ylab("Event Type")

ggsave('deaths_by_EVENT_TYPE_1996-2019.pdf')

```

### Frequency of storm events for every year in data set
##### saved as two seperate large pdfs for viewability
```{r fig.width=15, fig.height=8}
#   used to get the count of each event type by year, from dplyr package
byYear <- full %>% count(YEAR, EVENT_TYPE)

#   this is to limit the graph to storms that are most frequent using the first quartile
byYear <- subset(byYear, n > summary(byYear$n)[2])  

seq1 <- 1950:1995
seq2 <- 1996:2019

firstSet <- byYear[byYear$YEAR %in% seq1,]
secondSet <- byYear[byYear$YEAR %in% seq2,]

#   the second set contains so many types of events it had to be limited to storms that are most frequent using the first quartile
secondSet <- subset(secondSet, n > summary(secondSet$n)[2])

#   PLOT!!!!
#   1953-1995
ggplot(firstSet, aes(EVENT_TYPE, n))+
  geom_bar(stat='Identity')+
  coord_flip()+
  facet_wrap(vars(firstSet$YEAR))+
  ggtitle("Frequency of Events by Year", subtitle = "1953-1995")+
  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
  ylab("Frequency")+
  xlab("Event Type")

ggsave("event_frequency_1953-1995.pdf")

#   1996-2019
ggplot(secondSet, aes(EVENT_TYPE, n))+
  geom_bar(stat='Identity')+
  coord_flip()+
  facet_wrap(vars(secondSet$YEAR))+
  ggtitle("Frequency of Events by Year", subtitle = "1996-2019")+
  theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))+
  ylab("Frequency")+
  xlab("Event Type")

ggsave("event_frequency_1996-2019.pdf")

```

***
###### [back to navigation](#nav)
# <a name="sum"></a>
# Summary plots
### Most dangerous types of events in terms of directly related deaths
##### There are so many types of storms this data had to be subset to show the top most dangerous clearly
```{r}
e_by_d <- aggregate(DEATHS_DIRECT ~ EVENT_TYPE, FUN = sum, data=full)
sub <- subset(e_by_d, DEATHS_DIRECT > 200)  # arbitrary cutoff point

ggplot(sub, aes(reorder(EVENT_TYPE, DEATHS_DIRECT), DEATHS_DIRECT))+
  geom_bar(stat = 'identity')+
  ggtitle("Most Dangerous Events in Terms of Deaths")+
  theme(plot.title = element_text(hjust = 0.5))+
  ylab("Directly Related Deaths")+
  xlab("Event Types")+
  scale_y_continuous(label=comma)+
  coord_flip()
```


```{r}
e_by_d <- aggregate(INJURIES_DIRECT ~ EVENT_TYPE, FUN = sum, data=full)
sub <- subset(e_by_d, INJURIES_DIRECT > mean(e_by_d$INJURIES_DIRECT))  

ggplot(sub, aes(reorder(EVENT_TYPE, INJURIES_DIRECT), INJURIES_DIRECT))+
  geom_bar(stat = 'identity')+
  ggtitle("Most Dangerous Events in Terms of Injuries")+
  theme(plot.title = element_text(hjust = 0.5), plot.margin = margin(10, 15, 10, 10))+
  ylab("Directly Related Injuries")+
  xlab("Event Types")+
  scale_y_continuous(label=comma)+
  coord_flip()
```


### Most Costly Events in terms of property damage
##### subset based on the mean for viewability
```{r}
e_by_d <- aggregate(ADJ_DAMAGE_PROPERTY ~ EVENT_TYPE, FUN = sum, data=full)
sub <- subset(e_by_d, ADJ_DAMAGE_PROPERTY > mean(e_by_d$ADJ_DAMAGE_PROPERTY))  

ggplot(sub, aes(reorder(EVENT_TYPE, ADJ_DAMAGE_PROPERTY), ADJ_DAMAGE_PROPERTY))+
  geom_bar(stat = 'identity')+
  ggtitle("Most Costly Events in terms of Property Damage")+
  theme(plot.title = element_text(hjust = 0.5))+
  ylab("Property Damage Adjusted for Inflation ($)")+
  xlab("Event Types")+
  scale_y_continuous(label=comma)+
  coord_flip()
```


### Storm Events based on frequency
```{r}
#   obtain counts and subset out infrequent events using mean, again dplyr package
event <- full %>% count(EVENT_TYPE)
event <- subset(event, n >= mean(event$n)) 

#   PLOT!!!!
ggplot(event, aes(reorder(EVENT_TYPE, n), n))+
  geom_bar(stat='Identity')+
  ggtitle("Frequency of Events for the Past 70 Years")+
  theme(plot.title = element_text(hjust = 0.5))+
  ylab("Frequency")+
  xlab("Event Types")+
  coord_flip()+
  scale_y_continuous(labels = scales::comma)   # forces ggplot to stop using scientific notation for y-axis
```

### Pie chart of directly related deaths by state
```{r fig.width=4, fig.height=4}
tib_data <- as_tibble(full)

# clean state tib_data
newdata <- subset(tib_data %>% select (STATE, DEATHS_DIRECT), STATE %in% toupper(state.name))

# rank state in terms of death
dang_state <- aggregate(DEATHS_DIRECT~STATE, data=newdata, sum)
dang_state$dang_rank <- rank(-dang_state$DEATHS_DIRECT, ties.method='min',)

dang_state <- dang_state[order(dang_state$dang_rank),]

# subset states with less than 100 deaths
above100 <- subset(dang_state, dang_state$DEATHS_DIRECT > 100)

# rstudio pie chart
pie(above100$DEATHS_DIRECT, labels=dang_state$STATE, cex=0.6, main ='Total Amount of Direct Death per State, 1950 - 2019')

# ggplot pie chart
piepie <- ggplot(dang_state, aes(x="", y=dang_state$DEATHS_DIRECT,fill=dang_state$DEATHS_DIRECT))

above100$prob <- above100$DEATHS_DIRECT / sum(above100$DEATHS_DIRECT) * 100

ggplot(above100, aes(x="", y=prob, fill=STATE)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0)

```

***
###### [back to navigation](#nav)
# <a name="t1"></a>
# t.tests between 1996-2008 and 2009-2019 aggregated by Year-Month
##### NOAA only collected data on three types of severe storms until 1996 so in order to compare past data to more recent data we had to choose a date range that contained the same types of storms

##### Seperate data into two sets of years that contain similar types of events
```{r}
yrs1 <- 1996:2007
yrs2 <- 2008:2019

early <- full[full$YEAR %in% yrs1,]
late <- full[full$YEAR %in% yrs2,]

d_early <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=early )
d_late <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=late )

i_early <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=early )
i_late <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=late )

p_early <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=early )
p_late <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=late )


# check that there is a similar amount in each subset of data, they all have the same # of rows
nrow(d_early)
nrow(d_late)
```

### t.test of directly related deaths aggregated by year-month
##### H0 = there is no difference in directly related deaths between the two date ranges, 
##### HA = there is a significant difference in directly related deaths between the two date ranges
```{r}
hist(d_early$DEATHS_DIRECT)
hist(d_late$DEATHS_DIRECT)

t.test(d_early$DEATHS_DIRECT, d_late$DEATHS_DIRECT)
```

### t.test of directly related deaths aggregated by year-month, outliers removed.
##### doesn't change the conclusion just makes it less significant as one would expect.
```{r}
thresh <- 100

d_earlyOut <- subset(early, DEATHS_DIRECT < thresh)
d_lateOut<- subset(late, DEATHS_DIRECT < thresh)

d_early <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=d_earlyOut )
d_late <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=d_lateOut )

summary(d_early$DEATHS_DIRECT)
summary(d_late$DEATHS_DIRECT)

hist(d_early$DEATHS_DIRECT)
hist(d_late$DEATHS_DIRECT)

t.test(d_early$DEATHS_DIRECT, d_late$DEATHS_DIRECT)
```

### t.test of directly related injuries aggregated by year-month
##### H0 = there is no difference in directly related injuries between the two date ranges, 
##### HA = there is a significant difference in directly related injuries between the two date ranges
```{r}
hist(i_early$INJURIES_DIRECT)
hist(i_late$INJURIES_DIRECT)

t.test(i_early$INJURIES_DIRECT, i_late$INJURIES_DIRECT)
```

### t.test of directly related injuries, outliers removed
```{r}
thresh <- 600

i_earlyOut <- subset(early, INJURIES_DIRECT < thresh)
i_lateOut<- subset(late, INJURIES_DIRECT < thresh)

i_early <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=i_earlyOut )
i_late <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=i_lateOut )

summary(i_early$INJURIES_DIRECT)
summary(i_late$INJURIES_DIRECT)

hist(i_early$INJURIES_DIRECT)
hist(i_late$INJURIES_DIRECT)

t.test(i_early$INJURIES_DIRECT, i_late$INJURIES_DIRECT)
```


### t.test of damaged property aggregated by year-month
##### H0 = there is no difference in damaged property between the two date ranges, 
##### HA = there is a significant difference in damaged property between the two date ranges
```{r}
hist(p_early$ADJ_DAMAGE_PROPERTY)
hist(p_late$ADJ_DAMAGE_PROPERTY)

t.test(p_early$ADJ_DAMAGE_PROPERTY, p_late$ADJ_DAMAGE_PROPERTY)
```

### t.test of damaged property, outliers removed
```{r}
thresh <- 1000000000

p_earlyOut <- subset(early, ADJ_DAMAGE_PROPERTY < thresh)
p_lateOut<- subset(late, ADJ_DAMAGE_PROPERTY < thresh)

p_early <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=p_earlyOut )
p_late <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=p_lateOut )

summary(p_early$ADJ_DAMAGE_PROPERTY)
summary(p_late$ADJ_DAMAGE_PROPERTY)

hist(p_early$ADJ_DAMAGE_PROPERTY)
hist(p_late$ADJ_DAMAGE_PROPERTY)

t.test(p_early$ADJ_DAMAGE_PROPERTY, p_late$ADJ_DAMAGE_PROPERTY)
```

***
###### [back to navigation](#nav)
# <a name="t2"></a>
### t.tests of individual storms comparing directly related deaths, injuries, and property damage
##### H0 = there is no difference in directly related deaths, injuries, or property damage between the two date ranges
##### HA = there is a significant difference in directly related deaths, injuries, or property damage between the two date ranges

##### write a function to t-test a list of different storms removing outliers and aggregating by year-month
```{r}
test_storms <- function(events){
  
  return_list <- rep(NA, 1)
  return_count <- 1
  
  d_thresh <- 100
  i_thresh <- 600
  p_thresh <- 1000000000
  
  earlyOut <- subset(early, ADJ_DAMAGE_PROPERTY < p_thresh & INJURIES_DIRECT < i_thresh & DEATHS_DIRECT < d_thresh)
  lateOut<- subset(late, ADJ_DAMAGE_PROPERTY < p_thresh & INJURIES_DIRECT < i_thresh & DEATHS_DIRECT < d_thresh)
  
  for (event in events){
    eventEarly <- subset(earlyOut, EVENT_TYPE == event)
    eventLate <- subset(lateOut, EVENT_TYPE == event)
    
    d_eventEarly <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventEarly )
    d_eventLate <- aggregate( DEATHS_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventLate )
    
    i_eventEarly <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventEarly )
    i_eventLate <- aggregate( INJURIES_DIRECT ~ BEGIN_YEARMONTH, FUN=sum, data=eventLate )
    
    p_eventEarly <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=eventEarly )
    p_eventLate <- aggregate( ADJ_DAMAGE_PROPERTY ~ BEGIN_YEARMONTH, FUN=sum, data=eventLate )
    
    # cannot reject null
    d_test <- t.test(d_eventEarly$DEATHS_DIRECT, d_eventLate$DEATHS_DIRECT)
    
    # cannot reject null
    i_test <- t.test(i_eventEarly$INJURIES_DIRECT, i_eventLate$INJURIES_DIRECT)
    
    # cannot reject null
    p_test <- t.test(p_eventEarly$ADJ_DAMAGE_PROPERTY, p_eventLate$ADJ_DAMAGE_PROPERTY)
    
    test_list <- list("event" = event, "deaths" = d_test, "injuries" = i_test, "prop_damage" = p_test)
    return_list <- list.append(return_list, test_list)
  }
  return_list <- return_list[!is.na(return_list)]
  return(return_list)
}

```

##### create list and use function
```{r}
events <- c("Hurricane (Typhoon)", "Tornado", "Heat", "Flash Flood", "Flood", 
            "Thunderstorm Wind", "Hail", "High Wind", "Wildfire", "Lightning", "Rip Current")
test_storms(events)
```

### t.tests of individual storms comparing frequencies
##### H0 = storms are not more or less frequent between the two date ranges
##### HA = storms are more frequent in one of the date ranges than the other

##### write a function to t-test a list of different storms aggregating by year-month
```{r}
test_freq <- function(events){
  return_list <- rep(NA,1)
  for (event in events){
    col_name <- paste(event, "_freq", sep="")
    
    early[col_name] <- grepl(event, early$EVENT_TYPE)
    late[col_name] <- grepl(event, late$EVENT_TYPE)
    
    f_early <- aggregate(eval(as.name(col_name)) ~ BEGIN_YEARMONTH, FUN=sum, early)
    f_late <- aggregate(eval(as.name(col_name)) ~ BEGIN_YEARMONTH, FUN=sum, late)
    
    test_list <- list('event' = col_name, 'test' = t.test(f_early[,2], f_late[,2]))
    return_list <- list.append(return_list, test_list)
  }
  return_list <- return_list[!is.na(return_list)]
  return(return_list)
}
```

##### using the function to test list of storms except hurricanes
```{r}
# there are two different ways of putting hurricane into the data so that has to be handled seperately 
test_freq(events[2:length(events)])
```

##### testing hurricanes manually
```{r}
event <- "Hurricane"

early$hurricane_freq <- grepl(event, early$EVENT_TYPE)
late$hurricane_freq <- grepl(event, late$EVENT_TYPE)

f_early <- aggregate(hurricane_freq ~ BEGIN_YEARMONTH, FUN=sum, early)
f_late <- aggregate(hurricane_freq ~ BEGIN_YEARMONTH, FUN=sum, late)


t.test(f_early$hurricane_freq, f_late$hurricane_freq)
```

##### This code is looking at the three types of events we have consistently recorded throughout the 70 years: Hail, Thunderstorm Wind, and Tornadoes. The frequency of these events increase significantly in the later set of years from 1986-2019 but this is most likely due to better or more consistent recording practices by NOAA as opposed to these storms actually being that much more frequent. 
```{r}
yrs1 <- 1950:1985
yrs2 <- 1986:2019

early <- full[full$YEAR %in% yrs1,]
late <- full[full$YEAR %in% yrs2,]

early %>% count(EVENT_TYPE)
l <- late %>% count(EVENT_TYPE)
l[l$EVENT_TYPE %in% c("Hail", "Thunderstorm Wind", "Tornado"),]
```

***
###### [back to navigation](#nav)
# <a name="timeanal"></a>
# Time Series Analysis 
```{r}

### animated times series graph over years
year <- as.numeric(substr(full$BEGIN_YEARMONTH, 1, 4))
head(year)
table(year)         #  data includes records from 1950 - 2019

max(year) - min(year)  # equals 69, that's good!

full$year <- year
timess <- full %>% count(year)  # matches the values from table function (line 45)
plot(timess, ylab = 'number of records', type = 'l', main = 'Time series for all event types from 1950-2019')

givemedf <- as.data.frame(table(year))
givemedf$year <- as.integer(as.numeric(levels(givemedf$year))[givemedf$year])
#givemedf$year <- as.Date(as.character(givemedf$year), "%Y")

ggplot(givemedf, aes(year, Freq)) +             # animated time series plot for all event types
  geom_line() + 
  geom_point(size = 2) +
  transition_reveal(year) + 
  labs(title = "Year: {frame_along}", x = 'year', y = 'number of records') #+
  #anim_save('all_event_new.gif')             # Comment this line if you don't want to save it as a gif locally

fancy <- as.data.frame(head(sort(table(full$EVENT_TYPE)[table(full$EVENT_TYPE) > 10000],decreasing = T),9))
colnames(fancy)[1] <- "event_type"
fancy                               # Top 9 event types with most records
flag <- fancy[,1]
fullflag <- full[full$EVENT_TYPE==flag,]
table(fullflag$EVENT_TYPE,fullflag$year)      # 6 event types with 0 records until 1995 (Drought, Flash Flood, HS, HW, WS, WW)

givemedf2 <- as.data.frame(table(fullflag$EVENT_TYPE,fullflag$year))
givemedf2$Var2 <- as.integer(as.numeric(levels(givemedf2$Var2))[givemedf2$Var2])
#givemedf2$Var2 <- as.Date(as.character(givemedf2$Var2), "%Y")

ggplot(givemedf2, aes(Var2, Freq, group=Var1)) +              # animated time series plot for top 9 event types with most records
  geom_line(aes(col = Var1), size = 1) +
  scale_colour_discrete("event type") +
  geom_point(size = 2) +
  transition_reveal(Var2) + 
  labs(title = "Year: {frame_along}", x = 'year', y = 'number of records') #+
  #anim_save('top9_new.gif')       # Comment this line if you don't want to save it as a gif locally

### severe weather in Colorado predictive model
full2 <- full[(full$STATE=='COLORADO') & (full$year != 2019),]     # subset all Colorado records & get rid of 2019 (since no year-long data)
fancy <- as.data.frame(head(sort(table(full2$EVENT_TYPE),decreasing = T),9))     # Top 9 most happened severe weather in Colorado
colnames(fancy)[1] <- "event_type"                                    # Hail, Winter Storm, Winter Weather, Thunderstorm...
fancy

timess2 <- full2 %>% count(year)
plot(timess2, ylab = 'number of records', type = 'l', main = 'Time series for all event types happened in Colorado from 1950-2018')     # Time series plot for all severe weather types in Colorado

par(mfrow = c(3,3))
arnumber <- rep(NA, times=nrow(fancy))
pred19 <- rep(NA, times=nrow(fancy))
pred20 <- rep(NA, times=nrow(fancy))

for (i in 1:nrow(fancy)){
  fullsub <- full2[full2$EVENT_TYPE==fancy[i,1],]
  newtimess <- fullsub %>% count(year)
  newtimess <- as.data.frame(newtimess)
  plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[i,1]))
  ar1 <- ar(newtimess[,2])
  arnumber[i] <- ar1$order
  predRe <- predict(ar1,n.ahead = 2)
  pred19[i] <- predRe$pred[1]
  pred20[i] <- predRe$pred[2]
}



#################### Example of Hail (Colorado)
par(mfrow = c(1,1))
fullsub <- full2[full2$EVENT_TYPE==fancy[1,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[1,1]))

par(mfrow = c(1,2))
acf(newtimess[,2])
pacf(newtimess[,2])
## Since pacf displays a sharp cutoff and acf displays a slow decay, this series displays an "ar signature"
## by reading both pacf and acf, we are confident to use ar(15) model since PACF cut off is at lag 15.


#################### Example of Drought (Colorado)
par(mfrow = c(1,1))
fullsub <- full2[full2$EVENT_TYPE==fancy[6,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[6,1]))

par(mfrow = c(1,2))
acf(newtimess[,2])
pacf(newtimess[,2])

## We can see that for Drought, there is no strong geometry decay pattern for both acf and pacf, so it's not a good idea
## to use ar function on Drought (similar situation for High Wind, Flash Flood). It's also interesting for ar model of 
## this type of weather, the automatically selected order window is 0. And the 2019 prediction ^ 2020 prediction are the same.

arnumber_new <- arnumber
pred19_new <- floor(pred19)
pred20_new <- floor(pred20)
fancytype <- as.character(fancy[,1])

givemetable <- cbind(fancytype, arnumber_new, pred19_new, pred20_new)
colnames(givemetable)[1] <- "Event Type"
colnames(givemetable)[2] <- "Lag Window"
colnames(givemetable)[3] <- "2019 Prediction"
colnames(givemetable)[4] <- "2020 Prediction"
xtable(givemetable)

```

***
###### [back to navigation](#nav)
# <a name="predict"></a>
# Predictive Modelling
```{r}
### severe weather in Colorado predictive model
full2 <- full[(full$STATE=='COLORADO') & (full$year != 2019),]     # subset all Colorado records & get rid of 2019 (since no year-long data)
fancy <- as.data.frame(head(sort(table(full2$EVENT_TYPE),decreasing = T),9))     # Top 9 most happened severe weather in Colorado
colnames(fancy)[1] <- "event_type"                                    # Hail, Winter Storm, Winter Weather, Thunderstorm...
fancy

timess2 <- full2 %>% count(year)
plot(timess2, ylab = 'number of records', type = 'l', main = 'Time series for all event types happened in Colorado from 1950-2018')     # Time series plot for all severe weather types in Colorado

par(mfrow = c(3,3))
arnumber <- rep(NA, times=nrow(fancy))
pred19 <- rep(NA, times=nrow(fancy))
pred20 <- rep(NA, times=nrow(fancy))

for (i in 1:nrow(fancy)){
  fullsub <- full2[full2$EVENT_TYPE==fancy[i,1],]
  newtimess <- fullsub %>% count(year)
  newtimess <- as.data.frame(newtimess)
  plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[i,1]))
  ar1 <- ar(newtimess[,2])
  arnumber[i] <- ar1$order
  predRe <- predict(ar1,n.ahead = 2)
  pred19[i] <- predRe$pred[1]
  pred20[i] <- predRe$pred[2]
}
```

```{r}
#################### Example of Hail (Colorado)
par(mfrow = c(1,1))
fullsub <- full2[full2$EVENT_TYPE==fancy[1,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[1,1]))

par(mfrow = c(1,2))
acf(newtimess[,2])
pacf(newtimess[,2])
## Since pacf displays a sharp cutoff and acf displays a slow decay, this series displays an "ar signature"
## by reading both pacf and acf, we are confident to use ar(15) model since PACF cut off is at lag 15.
```

```{r}
#################### Example of Drought (Colorado)
par(mfrow = c(1,1))
fullsub <- full2[full2$EVENT_TYPE==fancy[6,1],]
newtimess <- fullsub %>% count(year)
newtimess <- as.data.frame(newtimess)
plot(newtimess, ylab = 'number of records', type = 'l', main = paste(fancy[6,1]))

par(mfrow = c(1,2))
acf(newtimess[,2])
pacf(newtimess[,2])

## We can see that for Drought, there is no strong geometry decay pattern for both acf and pacf, so it's not a good idea
## to use ar function on Drought (similar situation for High Wind, Flash Flood). It's also interesting for ar model of 
## this type of weather, the automatically selected order window is 0. And the 2019 prediction ^ 2020 prediction are the same.
```

```{r}
arnumber_new <- arnumber
pred19_new <- floor(pred19)
pred20_new <- floor(pred20)
fancytype <- as.character(fancy[,1])

givemetable <- cbind(fancytype, arnumber_new, pred19_new, pred20_new)
colnames(givemetable)[1] <- "Event Type"
colnames(givemetable)[2] <- "Lag Window"
colnames(givemetable)[3] <- "2019 Prediction"
colnames(givemetable)[4] <- "2020 Prediction"
xtable(givemetable)
```


***
# <a name="csv"></a>
###### [back to navigation](#nav)
# Creating the fullWeather.csv file from the downloaded NOAA files
### Rename files in details folder to allow for binding of data (only needed to be performed once)
```{r}
# ogfiles <- list.files("~/Documents/CU Boulder/2Masters/2Fall Semester/3-Quantitative-Methods/project/data/details")
# newfiles <- gsub("_c\\d{8}.csv.gz", '.csv', ogfiles)
# file.rename(ogfiles, newfiles)
```

### Bind files from every year together into dataframe labelled "full" (70 years worth of data!) then export to csv file for ease of use and sharing purposes (only needed to be performed once)
```{r}
# yearseq <- 1950:2019
# full <- read.csv('data/details/StormEvents_details-ftp_v1.0_d1950.csv')
# 
# 
# for(i in 2:length(yearseq)){
#     fpcur <- paste('data/details/StormEvents_details-ftp_v1.0_d', yearseq[i], '.csv', sep = "")
#     cur <- read.csv(fpcur)
#     full <- rbind(full, cur)
# }
# full <- as.data.frame(full)
# 
#   making sure all the data was combined correctly 
# summary(full$YEAR)
# unique(full$YEAR)
# head(full)
# unique(full$STATE)
# subset(full, STATE == "")                    *****there is one entry with no state*****
# 
# write.csv(full, file = "weatherFull.csv")
```































